System and method for optimizing animal production based on a target output characteristic

ABSTRACT

A system for generating optimized values for variable inputs to an animal production system. The system includes a simulator engine configured to receive a plurality of animal information inputs and generate a performance projection. At least one of the animal information inputs is designated as a variable input. The system further includes an enterprise supervisor engine configured to generate an optimized value for the at least one variable input based on an optimization criteria for at least one target output characteristic.

CROSS REFERENCE TO RELATED APPLICATIONS

This application is a continuation of U.S. application Ser. No.11/191,238, filed Jul. 27, 2005, which is a continuation-in-part of U.S.application Ser. No. 10/902,504, filed Jul. 29, 2004, the entirecontents of which are hereby incorporated by reference.

BACKGROUND

The present invention relates generally to the field of systems for andmethods of animal production. More particularly, the present inventionrelates to systems for and methods of optimizing animal production toproduce output having one or more nutrient characteristics.

An animal production system may include any type of system or operationutilized in producing animals or animal based products. Examples mayinclude farms, ranches, aquaculture farms, animal breeding facilities,etc. Animal production facilities may vary widely in scale, type ofanimal, location, production purpose, etc. However, almost all animalproduction facilities can benefit from identifying and implementingimprovements to production efficiency. Improvements to productionefficiency can include anything that results in increased productionresults, improved proportional output of desired products versus lessdesirable products (e.g. lean vs. fat), and/or decreased productioncosts.

A producer (i.e. a farmer, rancher, aquaculture specialist, etc.)generally benefits from maximizing the amount or quality of the productproduced by an animal (e.g. gallons of milk, pounds of meat, quality ofmeat, amount of eggs, nutritional content of eggs produced, amount ofwork, hair/coat appearance/health status, etc.) while reducing the costfor the inputs associated with that production. Exemplary inputs mayinclude animal feed, animal facilities, animal production equipment,labor, medicine, etc.

Animal feeds are compositions of a large variety of raw materials oringredients. The ingredients can be selected to optimize the amount ofany given nutrient or combination of nutrients in an animal feed productbased upon the nutrient composition of the ingredients used.

The nutritional composition of any one feed ingredient can be used incombination with the nutritional composition of every other ingredientin the feed to produce an animal feed that optimizes with a potentialmaximum or minimum evaluation criteria. One example of an evaluationcriteria is nutrient characteristics of output produced by an animal.For example, a particular cow feed composition can be made that willdeliver an improved balance of essential amino acids post ruminally.This has been shown to have the effect of increasing the protein contentof the cow's milk production.

What is needed is methods and systems for formulating an animal feed toproduce output having one or more characteristics. Further, there is aneed for a system and method to recommend changes to one or morevariable inputs to an animal production system to produce output havingone or more characteristics.

SUMMARY

One embodiment of the invention relates to a system for generatingoptimized values for variable inputs to an animal production system. Thesystem includes a simulator engine configured to receive a plurality ofanimal information inputs and generate a performance projection. Atleast one of the animal information inputs is designated as a variableinput. The system further includes an enterprise supervisor engineconfigured to generate an optimized value for the at least one variableinput based on an optimization criteria for at least one target outputcharacteristic.

Another embodiment of the invention relates to a method for determiningoptimized values for inputs to an animal production system. The methodincludes receiving a plurality of animal information inputs. At leastone of the animal information inputs is designated as a variable input.The method further includes receiving a target output characteristic,generating at least one performance projection based on the animalinformation inputs, and generating an optimized value for the at leastone variable input based on the at least one performance projection andthe target output characteristic and at least one optimization criteria.

Yet another embodiment of the invention relates to an animal productionoptimization system. The system includes an optimization engineconfigured to receive a target output characteristic. The optimizationengine has an objective function program. The system further includes ananimal production modeling system configured to receive animalinformation input, including at least one variable input, receive feedformulation input, and provide at least one modeling output to theoptimization engine. The modeling output includes a value for the targetoutput characteristic. The optimization engine utilizes the objectivefunction program to provide an optimized solution for the at least onevariable input based on the modeling output and the value for the targetoutput characteristic.

Other features and advantages of the present invention will becomeapparent to those skilled in the art from the following detaileddescription and accompanying drawings. It should be understood, however,that the detailed description and specific examples, while indicatingpreferred embodiments of the present invention, are given by way ofillustration and not limitation. Many modifications and changes withinthe scope of the present invention may be made without departing fromthe spirit thereof, and the invention includes all such modifications.

BRIEF DESCRIPTION OF THE DRAWINGS

The exemplary embodiments will hereafter be described with reference tothe accompanying drawings, wherein like numerals depict like elements,and:

FIG. 1 is a general block diagram illustrating an animal productionoptimization system, according to an exemplary embodiment;

FIG. 2 is a general block diagram illustrating an enterprise supervisorfor an animal production optimization system, according to an exemplaryembodiment;

FIG. 3 is a general block diagram illustrating a simulator for an animalproduction system, according to an exemplary embodiment;

FIG. 4 is a general block diagram illustrating an ingredients engine anda formulator for an animal production system, according to an exemplaryembodiment; and

FIG. 5 is a flowchart illustrating a method for animal productionoptimization, according to an exemplary embodiment.

DETAILED DESCRIPTION

In the following description, for the purposes of explanation, numerousspecific details are set forth in order to provide a thoroughunderstanding of the present invention. It will be evident to oneskilled in the art, however, that the exemplary embodiments may bepracticed without these specific details. In other instances, structuresand devices are shown in diagram form in order to facilitate descriptionof the exemplary embodiments.

In at least one exemplary embodiment illustrated below, a computersystem is described which has a central processing unit (CPU) thatexecutes sequences of instructions contained in a memory. Morespecifically, execution of the sequences of instructions causes the CPUto perform steps, which are described below. The instructions may beloaded into a random access memory (RAM) for execution by the CPU from aread-only memory (ROM), a mass storage device, or some other persistentstorage. In other embodiments, multiple workstations, databases,processes, or computers can be utilized. In yet other embodiments,hardwired circuitry may be used in place of, or in combination with,software instructions to implement the functions described. Thus, theembodiments described herein are not limited to any particular sourcefor the instructions executed by the computer system.

Referring now to FIG. 1, a general block diagram is shown illustratingan animal production optimization system 100, according to an exemplaryembodiment. System 100 includes an enterprise supervisor 200, asimulator 300, an ingredient engine 400, and a formulator 500.

System 100 may be implemented utilizing a single or multiple computingsystems. For example, where system 100 is implemented using a singlecomputing system, each of enterprise supervisor 200, simulator 300,ingredient engine 400, and formulator 500 may be implemented on thecomputing system as computer programs, discrete processors, subsystems,etc. Alternatively, where system 100 is implemented using multiplecomputers, each of enterprise supervisor 200, simulator 300, ingredientengine 400, and formulator 500 may be implemented using a separatecomputing system. Each separate computing system may further includehardware configured for communicating with the other components ofsystem 100 over a network. According to yet another embodiment, system100 may be implemented as a combination of single computing systemsimplementing multiple processes and distributed systems.

System 100 is configured to receive animal information input includingat least one variable input and analyze the received information todetermine whether variation in one or more of the variable inputs willincrease animal productivity or satisfy some other optimizationcriteria. Animal productivity may be a relative measure of the amount,type, or quality of output an animal produces relative to the expenseassociated with that production. Animal information input can includeany type of information associated with an animal production system. Forexample, animal information input may be associated with a specificanimal or group of animals or type of animals, an animal's environment,an economy related to the animal production, etc. Animal productivitymay further be configured to include positive and negative outputsassociated with the production. For example, animal productivity may beconfigured to represent harmful gaseous emissions as an expense (basedon either financial costs associated with clean up or the negativeimpact on the environment), reducing the overall productivity.

Information associated with a specific animal or a group or type ofanimals may include, but is not limited to, a species, a state, an age,a production level, a job, a size (e.g. current, target, variabilityaround, etc.), a morphology (e.g. intestinal), a body mass composition,an appearance, a genotype, a composition of output, a collection ofmicrobial information, health status, a color, etc. The informationassociated with a specific animal may be any type of informationrelevant for determining the productivity of the animal.

Species information can include a designation of any type or class ofanimals such as domestic livestock, wild game, pets, aquatic species,humans, or any other type of biological organism. Livestock may include,but is not limited to, swine, dairy, beef, equine, sheep, goats, andpoultry. Wild game may include, but is not limited to, ruminants, suchas deer, elk, bison, etc., game birds, zoo animals, etc. Pets mayinclude, but are not limited to, dogs, cats, birds, rodents, fish,lizards, etc. Aquatic species may include, but are not limited to,shrimp, fish (production), frogs, alligators, turtles, crabs, eels,crayfish, etc. and include those species grown for productive purposes(e.g., food products).

Animal state may include any reference or classification of animals thatmay affect the input requirement or production outputs for an animal.Examples may include, but are not limited to, a reproductive state,including gestation and egg laying, a lactation state, a health state orstress level, a maintenance state, an obese state, an underfed orrestricted-fed state, a molting state, a seasonal-based state, acompensatory growth, repair or recovery state, a nutritional state, aworking or athletic or competitive state, etc. Animal health states orstress level may further include multiple sub-states such as normal,compromised, post-traumatic (e.g. wean, mixing with new pen mates, sale,injury, transition to lactation, etc.), chronic illness, acute illness,immune response, an environmental stress, etc.

Animal age may include an actual age or a physiological state associatedwith an age. Examples of physiologic states may include a developmentalstate, a reproductive state including cycles, such as stage and numberof pregnancies, a lactation state, a growth state, a maintenance state,an adolescent state, a geriatric state, etc.

Animal job may include a physiologic state as described above, such asgestation, lactation, growth, egg production, etc. Animal job mayfurther include the animal's daily routine or actual job, especiallywith reference to canine and equines. Animal job may also include ananimal movement allowance, such as whether the animal is generallyconfined versus allowed free movement in a pasture, or, for an aquaticanimal, the different water flows the aquatic animal experiences, etc.

Animal size may include the actual weight, height, length,circumference, body mass index, mouth gape, etc. of the animal. Theanimal size may further include recent changes in animal size, such aswhether the animal is experiencing weight loss, weight gain, growth inheight or length, changes in circumference, etc.

Animal morphology includes a body shape exhibited by an animal. Forexample, a body shape may include a long body, a short body, a roundishbody, etc. Animal morphology may further include distinct measurement ofinternal organ tissue changes such as the length of intestinal villi,depth of intestinal crypts, and/or other organ sizes or shapes.

Animal body mass composition may include a variety of compositioninformation such as a fatty acid profile, a vitamin E status, a degreeof pigmentation, a predicted body mass composition, etc. The body masscomposition generally is a representation of the percentage or amount ofany particular component of body mass, such as lean muscle, water, fat,etc. The body mass composition may further include separaterepresentations composition for individual body parts/sections. Forexample, body mass composition may include edible component compositionssuch as fillet yield, breast meat yield, tail meat yield, etc.

Animal appearance may include any measure or representation of an animalappearance. Examples can include the glossiness of an animal's coat, ananimal's pigmentation, muscle tone, feather quality, feather cover, etc.

Animal genotype may include any representation of all or part of thegenetic constitution of an individual or group. For example, an animalgenotype may include DNA markers associated with specific traits,sequencing specific segments of DNA, etc. For example, the genotype maydefine the genetic capability to grow lean tissue at a specific rate orto deposit intramuscular fat for enhanced leanness or marbling,respectively. Additionally, genotype may be defined by phenotypicexpression of traits linked to genotypic capacity such as the innatecapacity for milk production, protein accretion, work, etc.

Composition of output may include the composition of a product producedby an animal. For example, the composition of output may include thenutrient levels found in eggs produced by poultry or milk produced bydairy cows, the amount, distribution, and/or composition of fat in meatproducts, a flavor and texture profile for a meat product,interrelationship between compositional part ratios, etc.

Microbial and/or enzyme information may include current microbialpopulations within an animal or within an animal's environment. Themicrobial and/or enzyme information may include measures of the quantityor proportion of gram positive or negative species or otherclassifications such as aerobes, anaerobes, salmonella species, E. colistrains, etc. Enzyme information may include the current content,quantity and/or composition of any enzyme sub-type or activation state,such as protease, amylase, and/or lipase, produced by the pancreas,produced within the gastrointestinal tract, enzymes produced by amicrobial population, a microbial community relationship at variousages, etc. Microbial and/or enzyme information may further includeinformation about potential nutritional biomass represented by thecurrent and/or a suggested microbial community that may be used as afeed source for some species (e.g., ruminants, aquatic species, etc.).The microbial and/or enzymatic environment may be monitored using any ofa variety of techniques that are known in the art, such as cpn60, othermolecular microbiological methods, and in vitro simulation of animalsystems or sub-systems.

Animal information input associated with an animal or group of animals'environment may include, but is not limited to, factors relatedspecifically to the environment, factors related to the animalproduction facility, etc. Animal environment may include any factors notassociated with the animal that have an effect on the productivity ofthe animal or group of animals.

Examples of animal information input related to the environment mayinclude ambient temperature, wind speed or draft, photoperiod or theamount of daylight exposure, light intensity, light wave length, lightcycle, acclimation, seasonal effects, humidity, air quality, waterquality, water flow rate, water salinity, water hardness, wateralkalinity, water acidity, aeration rate, system substrate, filtersurface area, filtration load capacity, ammonia levels, geographiclocation, mud score, etc. The environmental information may furtherinclude detailed information regarding the system containing the animalor animals, such as system size (e.g. the size in square meters, size insquare centimeters, hectares, acres, volume, etc.), system type (pens,cages, etc.), system preparation such as using liming, discing, etc.,aeration rate, system type, etc. Although some environmental factors arebeyond the control of a producer, the factors can usually be modified orregulated by the producer. For example, the producer may reduce draft byclosing vents, raise ambient temperature by including heaters or evenrelocating or moving certain animal production operations to a betterclimate for increasing productivity. According to another example, anaqua producer may modify nutrient inputs to an aquatic environment byaltering a feed design or feeding program for the animals in theenvironment. According to an exemplary embodiment, animal informationinput related to the environment may be generated automatically using anenvironmental appraisal system (EAS) to calculate a thermal impactestimate for an animal and to provide measurements for the animal'scurrent environment.

Examples of animal information input related to a production facilitymay include animal density, animal population interaction, feeder type,feeder system, feeder timing and distribution, pathogen loads, beddingtype, type of confinement, facility type, feathering, lightingintensity, lighting time patterns, time in holding pen, time away fromfeed, etc. Animal information input for a production facility may bemodified by a producer to increase productivity or address otherproduction goals. For example, a producer may build additionalfacilities to reduce population density, obtain additional or differenttypes of feeding systems, modify the type of confinement, etc.

Animal information input associated with economic factors may include,but is not limited to, animal market information. Animal marketinformation may include, but is not limited to, historical, currentand/or projected prices for outputs, market timing information,geographic market information, product market type (e.g., live orcarcass-based), etc.

Animal information inputs may further include any of a variety of inputsthat are not easily classifiable into a discrete group. Examples mayinclude an animal expected output (e.g., milk yield, productcomposition, body composition, etc.), a user defined requirement, a risktolerance, an animal mixing (e.g., mixing different animals), variationswith an animal grouping, etc., buyer or market requirements (e.g. Angusbeef, Parma hams, milk for particular cheeses, a grade for tuna, etc.),expected and/or targeted growth curves, survival rates, expected harvestdates, etc.

The above described animal information input may include informationthat is directly received from a user or operator through a userinterface, as will be described below with reference to FIG. 2.Alternatively, the animal information input or some part of the inputmay be retrieved from a database or other information source.

Further, some of the inputs may be dependent inputs that are calculatedbased on one or more other inputs or values. For example, an animal'sstress level may be determined or estimated based on population density,recent weight loss, ambient temperature, metabolic indicators such asglucose or cortisol levels, etc. Each calculated value may include anoption enabling a user to manually override the calculated value.Similarly, immune states may vary according to age, nutrient types andinput level, microbial challenges, maternal passive immunity provision,etc.

Yet further, each animal information input may include a variety ofinformation associated with that input. For example, each animalinformation input may include one or more subfields based on the contentof the animal information input. For example, where an indication isprovided that an animal is in a stressed state, subfields may bereceived indicating the nature and severity of the stress.

According to an exemplary embodiment, the animal information inputincludes a capability to designate any of the animal information inputsas a variable input. A variable input may be any input that a user hasthe ability to modify or control. For example, a user may designateambient temperature as a variable input based on the ability to modifythe ambient temperature through a variety of methods such as heating,cooling, venting, etc. According to an alternative embodiment, system100 may be configured to automatically recommend specific animalinformation inputs as variable inputs based on their effect onproductivity or satisfying the optimization criteria, as will be furtherdiscussed below with reference to FIG. 2.

Designation of a variable input may require submission of additionalinformation, such as a cost and/or benefit of variation of the variableinput, recommended degrees of variation for optimization testing, etc.Alternatively, the additional information may be stored and retrievablefrom within system 100 or an associated database.

The animal information inputs may further include target values as wellas current values. A target value may include a desirable level foranimal productivity or some aspect of animal productivity. For example,a producer may wish to target a specific nutrient level for eggsproduced by poultry. Therefore, the producer may enter current nutrientlevels for eggs currently being produced as well as target nutrientvalues for the eggs. According to another example, a current sizebreakdown for shrimp in a pond versus a potential size breakdown. Thetarget values and current values may be utilized by system 100 to makechanges in an animal feed formulation or to make changes to variableinputs as will be described further below. Further, the target valuesmay be viewed as equality constraints and/or inequality constraints forthe optimization problem.

Table 1 below lists exemplary animal information inputs that may beprovided as inputs to animal production optimization system 100. Thislisting of potential animal information inputs is exemplary and notexclusive. According to an exemplary embodiment, any one or more of thelisted animal information inputs can be designated as a variable input.

TABLE 1 General Characteristics Impact of the ration on the greaterQuantity and/or composition Quantity and/or environment: (e.g. nitrogen,phosphorus, composition of urine etc.) of manure or litter per animalQuantity and/or quality of odor from facility Swine Characteristics Sowreproductive performance No. of pigs born No. of pigs born alive No. ofpigs weaned Piglets birth weight Uniformity of baby pigs Mortality ofbaby pigs Piglets weaning weight Sow body condition score Sow lactationback fat loss Sow lactation weight loss Interval weaning to estrus Sowlongevity Working boar Body condition score Working frequency Semenquality Finisher Average daily gain Average daily lean gain Averagedaily feed intake per weight gain Average daily feed intake per Feedwastage Feed form lean gain Mortality Days to market Feed cost per kggain Feed cost per kg lean gain Medication usage per pig Dressingpercentage Lean percentage Back fat thickness Fatty acid compositionEvaluation Criteria for Environment Thermal environment (Draft, Airquality (Dust, Humidity, Pig/pen Floor type, Bedding, Insulation)Ammonia, Carbon dioxide, etc) Pig density Health condition Feeder typePigs/feeder hole Water quality and quantity Immune status EvaluationCriteria for appearance Hair coat condition Skin color Ham shape Bodyshape and length Evaluation Criteria for meat/fat quality Meat and fatcolor Iodine value Fatty acid profile PSE Juiciness Flavor TendernessMarbling score Water holding capacity Evaluation Criteria for HealthSuckling piglets Eye condition (dry and dirty or Skin condition (elasticor Hair condition (dense or bright and vital eyes) dry) and color (pinkor coarse) pale) Dirtiness of around anus Breathe with open mouth Bellycondition Finisher Respiratory disease Body temperature Cannibalism(tail, ear, belly biting) Skin and hair condition (mange Stool conditionSwollen knee and ankle and parasites) joint Dirtiness of around eyesNose condition Respiratory sound (Difficulties in breathing) ActivityMicrobial profile or levels Sows MMA (Mastitis, Metritis, Stoolcondition Abortion and stillbirth Agaclactia) (constipation) Wet bellyBody shaking Vaginal and uterine prolapse Body condition score Intervalweaning to estrus Feed intake (sick sows eat less) Leg problem Bodytemperature Dairy Characteristics Cow reproductive performance Breedingper conception Live birth Days to first estrous Calf birth weight Daysopen Days to cleaning Calf weaning weight Cow body condition score MUNand BUN Cow body reserve change Calving interval Blood hormonesprogesterone and estrogen Lactation Milk per day Body fatty acid loss orgain Average daily feed intake per kg milk Feed wastage Feed formMortality Lactation length Feed cost per kg milk Milk per year andlifetime milk Morbidity Body amino acid loss or gain Fatty acidcomposition of milk (CLA, EPA and DHA, 18:2 to 18:3 ratio of milk)Evaluation Criteria for Environment Thermal environment (Draft, Airquality (Dust, Humidity, Blood cortisol, NEFA Floor type, Bedding,Insulation) Ammonia, Carbon dioxide, etc) Animal density Healthcondition Feed presentation method Cows per bunk or waterer Waterquality and quantity Cow care and comfort space score card EvaluationCriteria for appearance Hair coat condition Skin color Body conditionscore Body shape and length Color of mucus membrane Appearance of eyesand ears Evaluation Criteria for milk quality Milk color Milk proteincomposition Milk fat yield Milk flavor Milk lactose Milk protein yieldMilk fatty acid composition Total milk solids Evaluation Criteria forHealth Calves Eye condition (dry and dirty or Skin condition (elastic orHair condition (dense or bright and vital eyes) dry) and color (pink orcoarse) pale) Dirtiness of around anus Breathe with open mouth Bellycondition Body Temperature Heifers Respiratory disease Body temperatureSkin and hair condition (mange Stool condition Swollen knee and ankleand parasites) joint Dirtiness of around eyes Nose condition Respiratorysound (Difficulties in breathing) Activity Cows Mastitis, Metritis Stoolcondition Abortion and stillbirth (constipation) manure screener Bloodmeasures EX: cortisol, Body shaking Vaginal and uterine NEFA, BHBA,alkaline prolapse phosphitase, progesterone estrogen bun Body conditionscore Calving interval Feed intake (sick cows eat less) Leg problem Bodytemperature Milk urea nitrogen Companion Animal and EquineCharacteristics Hair coat shine Hair coat-fullness Skin scale/flakelevel Fecal consistency Gas production Breath Immune status Antioxidantstatus Body condition (thin, normal, obese) skeletal growth rateEndurance Digestive health status Circulatory health status Hoof qualityHair quality Body fluid status Workload (NRC specifies light, medium andheavy workloads) Characteristics to optimize for athlete animals: SpeedSprint Muscular glycogen spare Muscular glycogen recovery Decreaserecovery time Endurance after exercise Body condition Health and Welfareof the Animal: Welfare and behavior (calmer or Relationship between Drymatter intake energetic diet): NDF/starch or forage/grain Long fiberintake Electrolytes General health status: Low allergenicity Digestivehealth Improving immunologic Increasing antioxidant status statusMinimize digestive upset Immunologic status Beef Characteristics Cowreproductive performance Conception rate Weaning rate Calf birth weightCalf mortality Calf weaning weight Cow body condition score Intervalweaning to estrus Calving interval Bulls Body condition score BreedingSoundness Growing and Finishing Average daily lean gain Average dailyfeed intake Feed cost per unit gain per gain Feed cost per unit leangain Stocking Rate Evaluation Criteria for Environment Air qualityNutrient excretion Evaluation Criteria for appearance Hair coatcondition Height Height/weight ratio Evaluation Criteria for meat/fatquality Meat and fat color Fatty acid profile Juiciness FlavorTenderness Marbling score Dressing percentage Red meat yield Muscle pHIntra muscular fat Antioxidant status Evaluation Criteria for HealthMortality Medication cost Morbidity Poultry Characteristics Egg andreproduction Egg number Fertility Hatchability Egg weight Egg mass Egginternal quality (Haugh Units) Egg yolk color Eggshell quality Eggbacteriological content (Salmonella-fee) Fertile eggs breakout analysisPerformance Average daily gain Average daily feed intake Feed conversionMortality Occurrences of Leg Feed cost per kg gain live problem weightFeed cost per dozen eggs Yield of Eviscerated Yield of body partscarcass (breast, thigh, back etc.) Flock Uniformity Feed consumptionEnvironment Temperature Air quality (Dust, Bird density Humidity,Ammonia, Carbon dioxide, etc) Feeder space Lighting program Waterquality and quantity Litter quality (Wet droppings) Biosecurity ImmuneStatus Microbial profile or levels Evaluation Criteria for appearanceFeathering score Skin color Skin scratching score Feed appearance(color, texture, etc.) Aquaculture Animal Characteristics Initial weightSize variability Developmental stage Target weight Stocking density Bodycomposition (or meat composition) Body condition Animal or meat colorSurvival rate Feedings per day Feeding activity Swimming Speed Feedwater stability Desired shelf-life Specific growth rate Meat yield(e.g., fillet, tail meat, Mouth gape Cost per unit gain etc.) FCR Daysto market Genotype Pigmentation Feed Consumption Harvest Biomass Numberof days to “X” animal $ cost/unit weight gain % of yield of target sizeproduct (shrimp tails, fillet, etc.) $ profit/unit production biomassReturn on investment Cycles per year $ of feed/unit weight of $ offeed/$ of biomass Total harvest biomass production % of animals intarget size range Mortality rate Product shelf life Average animal size$ of profit/unit of culture Average weight gain/week area or volumeWeight of production/unit of Species Days of culture (stocking aerationdate) Aquaculture Environmental Characteristics System type and sizeAmmonia, pH, dissolved Water flow rate oxygen, alkalinity, temp.,hardness, etc. Water exchange rate Nutrient load Natural productivitybiomass (species specific forage base) Population health Environmentalpathogen Temperature, oxygen, etc. load variability System SubstrateWater Filtration Rate Feed on feeding tray Total Filtration CapacityPhotoperiod Processing form for feed (Mechanical and Chemical) Medicineapplication Aeration rate Nitrogen level Aeration pattern Feeding tray #and Feed distribution pattern positioning Secchi disc reading Immunestatus Microbial profile or levels Phosphorus level

Referring now to the components of system 100, supervisor 200 may be anytype of system configured to manage the data processing function withinsystem 100 to generate optimization information, as will be furtherdiscussed below with reference to FIG. 2. Simulator 300 may be any typeof system configured to receive animal information or animal formulationdata, apply one or more models to the received information, and generateperformance projections such as animal requirements, animal performanceprojections, environmental performance projections, and/or economicperformance projections as will be further discussed below withreference to FIG. 3. Ingredient engine 400 may be any kind of systemconfigured to receive a list of ingredients and generate ingredientprofile information for each of the ingredients including nutrient andother information. Formulator 500 may be any type of system configuredto receive an animal requirements projection and ingredient profileinformation and generate animal formulation data, as will be furtherdiscussed below with reference to FIG. 4.

Referring now to FIG. 2, a general block diagram illustrating anenterprise supervisor 200 for an animal production optimization system100 is shown, according to an exemplary embodiment. Enterprisesupervisor 200 includes a user interface 210 and an optimization engine230. Enterprise supervisor 200 may be any type of system configured toreceive animal information input through user interface 210, submit theinformation to simulator 300 to generate at least one animalrequirement, submit the at least one animal requirement to formulator500 to generate least cost animal feed formulation given the animalrequirement, submit the optimized formulation to simulator 300 togenerate a performance projection and to utilize optimization engine 230to generate optimized values for one or more variable inputs.

According to an alternative embodiment, optimization or some portion ofthe optimization may be performed by a different component of system100. For example, optimization described herein with reference tosupervisor 200 may alternatively be performed by simulator 300. Further,optimization of animal feed formulation may be performed by formulator500.

Enterprise supervisor 200 may include or be linked to one or moredatabases configured to automatically provide animal information inputsor to provide additional information based upon the animal informationinputs. For example, where a user has requested optimization informationfor a dairy production operation, enterprise supervisor 200 may beconfigured to automatically retrieve stored information regarding theuser's dairy operation that was previously recorded to an internaldatabase and also to download all relevant market prices or otherrelevant information from an external database or source.

User interface 210 may be any type of interface configured to allow auser to provide input and receive output from system 100. According toan exemplary embodiment, user interface 210 may be implemented as a webbased application within a web browsing application. For example, userinterface 210 may be implemented as a web page including a plurality ofinput fields configured to receive animal information input from a user.The input fields may be implemented using a variety of standard inputfield types, such as drop-down menus, text entry fields, selectablelinks, etc. User interface 210 may be implemented as a single interfaceor a plurality of interfaces that are navigable based upon inputsprovided by the user. Alternatively, user interface 210 may beimplemented using a spreadsheet based interface, a custom graphical userinterface, etc.

User interface 210 may be customized based upon the animal informationinputs and database information. For example, where a user defines aspecific species of animal, enterprise supervisor 200 may be configuredto customize user interface 210 such that only input fields that arerelevant to that specific species of animal are displayed. Further,enterprise supervisor 200 may be configured to automatically populatesome of the input fields with information retrieved from a database. Theinformation may include internal information, such as stored populationinformation for the particular user, or external information, such ascurrent market prices that are relevant for the particular species asdescribed above.

Optimization engine 230 may be a process or system within enterprisesupervisor 200 configured to receive data inputs and generateoptimization information based on the data inputs and at least one ofthe optimization criteria. According to an exemplary embodiment,optimization engine 230 may be configured to operate in conjunction withsimulator 300 to solve one or more performance projections and calculatesensitivities in the performance projection. Calculating sensitivitiesin the performance projections may include identifying animalinformation input or variable inputs that have the greatest effect onoverall productivity or other satisfaction of the optimization criteria.Optimization engine 230 may further be configured to provide optimizedvalues for the animal information inputs or variable inputs based on thesensitivity analysis. Optimization may include any improvement toproductivity or some other measure according to the optimizationcriteria. The process and steps in producing the optimized values arefurther discussed below with reference to FIG. 5.

Optimization criteria may include any criteria, target, or combinationof targets or balanced goals that are desirable to the current user. Ina preferred embodiment, the optimization criteria is maximizingproductivity. Maximizing productivity may include maximizing a single ormultiple factors associated with productivity such as total output,output quality, output speed, animal survival rates, etc. Maximizingproductivity may further include minimizing negative values associatedwith the productivity, such as costs, harmful waste, etc. Alternativeoptimization criteria may include profitability, product quality,product characteristics, feed conversion rate, survival rate, growthrate, biomass/unit space, biomass/feed cost, cost/production day,cycles/year, etc. Alternatively, the optimization criteria may includeminimizing according to an optimization criteria. For example, it may bedesirable to minimize the nitrogen or phosphorus content of animalexcretion.

Where the optimization criteria is used to optimize a target outputcharacteristic, the target value may be a desired value for acharacteristic of some output produced by the animal production system.For example, a dairy producer may desire a milk output product havingenhanced milk protein. A milk output product having increased proteinconcentration can increase cheese yield, making the output product morevaluable for a cheese producer. To capture this value, the animalproducer may, for example, utilize system 100 to obtain a recommendationfor modifications to one or more of the variable inputs to generate adiet using amino acid metabolism concepts that will lead to a 0.3%increase in milk protein in animals fed the diet. Another producer mayseek milk production that is especially low in fat content to createyogurt. Similar to the milk with increased protein content that diet maybe tailored to produce the output having the low fat characteristic.Another desirable characteristic may be a high level of polyunsaturatedfat, represented by the amount of linolenic acid C18:3 in milk or animalmeat to make the output product healthier for the eventual consumer.Other animal information inputs may also be varied to produce the outputhaving the desired characteristics.

The target output characteristics may also be used to generaterecommendations to configure the animal production system to produceoutput that has reduced or minimized characteristics. The minimizedcharacteristics may be advantageous in reducing harmful or detrimentalcharacteristics of the output. For example, dairy production wastegenerally has high levels of nitrogen and phosphorus that are regulatedby stringent environmental standards. Animal producers often face highcosts ensuring compliance with these standards. Accordingly, system 100may be configured such that the total output product, the amount ofwaste, or a characteristic of the output product, the nitrogen andphosphorus levels in the waste, is reduced. Producing the optimizedwaste may include analyzing the nutrients being fed to an animal toavoid overfeeding digestible phosphorus and balancing rumen and cowmetabolism to maximize nitrogen retention. Although the analysis mayyield clear recommendations, producing optimized waste may requireanalyzing or presenting opposing recommendations and their projectedeffects to facilitate the balancing of mutually exclusive advantagesbetween an increase in animal performance and reduced waste managementcosts.

Managing phosphorus characteristics in output may additionally provideadvantages in an aquaculture production system. Phosphorus is animportant macromineral for the skeletal development of fish species andkey metabolic nutrient for growth and proper metabolism for all aquaspecies. Insufficient dietary phosphorus in aquafeeds can lead todepression of growth and skeletal formation for aqua species. However,phosphorus is also a key limiting nutrient in freshwater aquaculturesystems and excess dietary phosphorus can quickly lead to overproductionof algae causing instability to the health of the system. Excessphosphorus is also undesirable because it is an unnecessary cost.

A formulation system can use available phosphorus nutrient in an aquaticenvironment in conjunction with a phosphorus nutrient in the animal feedformulation generated by system 100 to meet the needed animalrequirement with highly available sources and optimize the excessphosphorus entering the aquatic environment. Empirical data from animaldigestibility or environmental samples may be used to increase theprecision by which this nutrient is managed in the formulation process.

According to another exemplary embodiment, the targeted characteristicmay be the nutrient composition of an aquatic meat product. For example,the targeted characteristic may be the fatty acid profile of the meatproduct. Aquatic meat products have received considerable recognitionfor generally containing a healthier profile of fatty acids for humandiet than many terrestrial meat sources. The composition of fatty acidsin these aquatic meats have largely been based on normal deposition thatoccurs from consumption of natural foods or artificial feeds, whichoften contain these fatty acids to meet the animal's requirements.Accordingly, system 100 may be configure to generate and animal feedformulation having an array of fatty acids that, when fed to a targetculture species, results in an improved fatty acid profile, i.e., morebeneficial to human health. A similar example would involve the use ofhigher levels of vitamin E and selenium to impart an increasedshelf-life to the fillet.

The targeted characteristic may also be non-nutrient related. Forexample, changing the free amino acid content of meat to change itsflavor, limiting the concentrations of or choosing improvedbioavailability of nutrients that become toxic when they accumulate inzero water exchange systems, targeting specific levels of beta-carotene,astaxanthin or other pigments that can be used metabolically as ananti-oxidant, Vitamin A precursor, or to impart coloration to the meator skin, etc.

Target output characteristics may include, but are not limited to, endproduct composition or characteristics including meat yield as apercentage of body weight, saleable product yield, yield of specificbody parts, fatty acid profile, amino acid content, vitamin content,marbling, iodine value, water holding capacity, tenderness, body orproduct color, pigment level, body or product shelf life, etc. Thetarget output characteristic may also include, but is not limited to, awaste composition or environmental effect, including uneaten foodamounts, leaching or loss of nutrients such as nitrogen, ammonia,phosphorus, vitamins, attractants, etc., fecal consistency,fecal/urinary output, including total output, ammonia or nitrogen loadin system, phosphorus load in system, organic matter bypass, etc.,biological oxygen demand, bypass energy, gaseous emissions, C/N ratio ofwaste stream etc. Although the above examples are provided, a person ofordinary skill in the art can recognize that the target outputcharacteristic may be any output generated in a production system.

Advantageously, system 100 may optimize across all variable animalinformation inputs to generate recommendations for producing the outputhaving specified target characteristics at the lowest cost. Therecommendation may include a single optimal recommendation or aplurality of recommendations yielding equivalent benefits.

Optimization engine 230 may be configured to implement its ownoptimization code for applications where feed ingredient informationfrom formulator 500 is combined with other information and/orprojections calculated in simulator 300. Optimization problems thatcoordinate several independent calculation engines, referred to asmultidisciplinary optimizations, may be solved using gradient-basedmethods, or more preferably simplex methods such as Nelder-Mead orTorczon's algorithm. Preferably, optimization engine 230 may beconfigured to implement a custom combination of a gradient-based methodfor variables on which the optimization criteria depends smoothly(decision variables fed to simulator 300) and a simplex method forvariables on which the objective function has a noisy or discontinuousdependence (diet requirements fed to formulator 500). Alternatively,other optimization methods may be applied, including but not limited to,pseudo-gradient based methods, stochastic methods, etc.

Enterprise supervisor 200 may be further configured to format theoptimization results and provide the results as output through userinterface 210. The results may be provided as recommended optimizedvalues for the variable inputs. The results may further includerecommended values for additional animal information inputs, independentof whether the animal information input was designated as a variableinput. The results may further include a projection of the effects ofimplementation of the optimized values for the variable inputs.

Enterprise supervisor 200 may be configured to implement a Monte Carlomethod where a specific set of values is drawn from a set ofdistributions of model parameters to solve for optimized values for thevariable inputs. This process may be repeated many times, creating adistribution of optimized solutions. Based on the type of optimization,enterprise supervisor 200 maybe used to select either the value mostlikely to provide the optimal solution or the value that givesconfidence that is sufficient to meet a target. For example, a simpleoptimization might be selected which provides a net energy level thatmaximizes the average daily gain for a particular animal. A Monte Carlosimulation may provide a distribution of requirements including variousnet energy levels and the producer may select the net energy level thatis most likely to maximize the average daily gain.

Enterprise supervisor 200 may further be configured to receive realworld empirical feedback based on the application of the optimizedvalues for the variable inputs. The empirical feedback may be used toadjust the variable inputs to further optimize the animal productionsystem. The empirical feedback may further be compared to theperformance projections to track the accuracy of the projections.Empirical feedback can be provided using any of a variety of methodssuch as automated monitoring, manual input of data, etc.

Empirical feedback may be any type of data that is gathered or generatedbased on observations. The data may be gathered by an automated systemor entered manually based on a users observations or testing. The datamay be gathered in real-time or on any periodic basis depending on thetype of data that is being gathered. This data may also already berepresented in the animal information inputs and be updated based on anychanging values. The empirical feedback to be monitored will generallyinclude animal information inputs that impact an animal productionsystem product, herd health, etc. on a daily basis. The empiricalfeedback may include, but is not limited to, environment information,animal comfort information, animal feed information, production systemmanagement information, animal information, market conditions or othereconomic information, etc. For example, in a beef production system, theempirical feedback may include carcass data, linear measurements,ultrasound measurements, daily intakes, etc.

Environment information may include information regarding the animal'senvironment that may affect animal productivity. For example,temperatures above the thermo-neutral zone may decrease an animal's feedintake. Temperature may also affect a rate of passage, which in turn mayhave an effect on nutrient digestibility, bypass of protein/amino acids,nutrients in excretion, etc. Temperature may also increase intake ofanimal feed. For example, wind in cold temperatures will increasemaintenance energy for warmth (shivering).

The environmental information may also include non-temperatureinformation. For example, in warm temperatures, wind can assist incooling requiring less loss of dry matter intake, less energy wasted incooling attempts (panting). Similarly, increasing relative humidity maydecrease cow comfort based on an increased heat load when thetemperature is warm/hot.

The empirical feedback may further be dependent on the cow'senvironment. For example, weather events (sun, snow, rain, mud, etc.)are important for cows housed outside. Weather events can impact thebody temperature of the cow and the animal's need for shivering orpanting further impacting intakes, digestibility, etc. If cows travelfrom pasture to parlor, mud or stormy/snowy weather can impact theamount of energy required to get to the parlor and back, raisingmaintenance requirements.

Other environmental information may be related to the general quality ofthe animal's environment and the level of stress placed on the animal.For example, animal crowding can have a strong impact on an animal'sproductivity. In overcrowding conditions, dominant cows will get feedfirst and remaining cows will get a sorted feed which contains differentnutrients than formulated feed. Further, cows also need to spend acertain amount of time lying down in order to maximize production. Yetfurther, overcrowding may cause cows to lie in alleys resulting inincreased potential of stepped on teats and mastitis or stand too long.Other exemplary environmental information may include the amount oflight, access to water and feed, proper bedding and stalls to encouragecows to lie down, milking protocol such that cows are not held in aholding pen longer than one hour at a time, etc.

Although the above examples are provide in reference to a cow, it shouldbe understood that the described system and method can be similarlyapplied to any animal. For example, poultry animals may similarly facestress and/or less than optimal growth based on increased temperature.This additional stress can be reduce by, for example, increasing fan useto cause a direct wind, using intermittent misting, etc.

Other empirical feedback may include analysis of the actual animal feedbeing consumed by animals. For example, a sample may be taken from theanimal feed as it is being fed to animals to analyze the nutrientcontent and assure that the diet being fed is the diet that wasformulated to optimize production. The analysis may include an analysisof ingredients as the arrive at the animal production system. To reduceexcessive deviation from a formulated animal feed, more variableingredients can be used at lower inclusion rates. Similarly, empiricaltesting may include analysis of the ingredients found naturally at theanimal production facility, such as the quality of the water ingested bythe animals. Water may deliver some minerals in various amounts or havea specific pH level that should be accounted for in diet formulations

Empirical testing may further include monitoring the managementpractices of the animal production system. Management practice mayinclude feed timing, personnel, production gathering practices, etc. Forexample, an animal production systems personnel may have an affect onproduction by having an effect on cow comfort level. The number ofpeople, their experience level, the time it takes to complete tasks,etc. can all impact cow comfort.

Animal management practices also may be monitored. Animal managementpractices may include any practices that may have an effect on theanimals. For example, animal production may be affected be feeding timepractices. Feeding timing can impact that quality of feed provided,especially in hot weather. The system may be further configured tomonitor the frequency and duration of time during which feed is providedto the animal such that the animal is able to eat.

Animal production gathering practices may also have an effect. Animalproduction gathering may include any process to obtain the results ofthe animal production, such as the number of milkings per day, egggathering frequency, etc. that will influence production potential. Moremilkings may increase production in well-managed herds. It may also bebeneficial to increase milkings in cows just starting their lactationsto facilitate production.

Empirical testing may further include monitoring the animals within theanimal production system. For example, an animal may be monitored formetabolic indicators. Metabolic indicators may be indicative ofmetabolic problems such as milk fever, ketosis, imbalances in dietaryprotein, overheating, etc. Other monitored characteristics may includecharacteristics that must be tested within a laboratory such asnon-esterified fatty acids (NEFA), beta hydroxyl butyrate (BHBA), urinepH, milk urea nitrogen (MUN), blood urea nitrogen (BUN), bodytemperature, blood AA, manure characteristics, carbon dioxide levels,minerals, fat pad probes for pesticide residue testing, etc. Othercharacteristics may be monitored through observation, such as animals inheat, limping animals, sick animal, pregnancy, etc. that may not eat andproduce as well as normal. Yet other characteristics may be acombination of these categories. Other physiological measurements mayinclude microbial profile or hut histological measurements.

Empirical testing provides the advantage of verifying the accuracy ofpredictive models generated by simulator 300. Optimization resultsgenerated from imperfect models may different from real world resultsobtained through empirical testing. System 100 may be configured toprovide dynamic control based on the empirical testing feedback,adjusting animal information inputs or generate values, such as ananimal's feed formulation, to achieve specified targets based on thedifference between model results and empirical testing feedback.Further, simulator 300 may be configured to adjust how models aregenerated based on the data obtained through the empirical testing toincrease the accuracy of future models.

Further, enterprise supervisor 200 may be configured to enable dynamiccontrol of models. After setting an initial control action, for examplethe feed formulation, as will be discussed below with reference to FIG.5, the animal response may be monitored and compared with theprediction. If the animal response deviates too far from the prediction,a new control action, e.g., feed formulation, may be provided. Forexample, if the performance begins to exceed prediction, some value maybe recovered by switching to a less costly feed formulation, differentwater flow rate, etc. If performance lags prediction, switching tohigher value feed formulation, may help to ensure that the final producttargets are met. Although the control action is described above withreference to a feed formulation, the control action may be for anycontrol variable, such as water flow rate, feeding rate, etc. Similarly,the adjustments may be made to that control variable, such as byincreasing or decreasing the flow rate, etc.

Referring now to FIG. 3, a general block diagram illustrating asimulator 300 is shown according to an exemplary embodiment. Simulator300 includes a requirements engine 310, an animal performance simulator320, an environment performance simulator 330, and an economicperformance simulator 340. Generally, simulator 300 may be any processor system configured to apply one or more models to input data toproduce output data. The output data may include any type of projectionor determined value, such as animal requirements and/or performanceprojections, including animal performance projections, economicperformance projections, environmental performance projections, etc.

Specifically, simulator 300 is configured to receive animal informationinput from enterprise supervisor 200, process the information usingrequirements engine 310 and an animal requirements model to produce aset of animal requirements. Further, simulator 300 may be configured toreceive feed formulation data from enterprise supervisor 200 and processthe feed formulation data using any combination of animal performancesimulator 320, environment performance simulator 330, and economicperformance simulator 340 to produce at least one performanceprojection.

An animal requirements model, used by simulator 300 to convert inputvalues into one or more outputs, may consist of a system of equationsthat, when solved, relate inputs like animal size to an animalrequirement like protein requirement or a system requirement like spaceallotment or feed distribution. A specific mathematical form for themodel is not required, the most appropriate type of model may beselected for each application. One example is models developed by theNational Research Council (NRC), consisting of algebraic equations thatprovide nutrient requirements based on empirical correlations. Anotherexample is MOLLY, a variable metabolism-based model of lactating cowperformance developed by Prof. R. L. Baldwin, University ofCalifornia-Davis. A model may consist of a set of explicit ordinarydifferential equations and a set of algebraic equations that depend onthe differential variables. A very general model may consist of a fullyimplicit, coupled set of partial differential, ordinary differential,and algebraic equations, to be solved in a hybrid discrete-continuoussimulation.

A model may be configured to be independent of the functionalityassociated with simulator 300. Independence allows the model and thenumerical solution algorithms to be improved independently and bydifferent groups.

Preferably, simulator 300 may be implemented as an equation-basedprocess simulation package in order to solve a wide variety of modelswithin system 100. Equation-based simulators abstract the numericalsolution algorithms from the model. This abstraction allows modeldevelopment independent from numerical algorithms development. Theabstraction further allows a single model to be used in a variety ofdifferent calculations (steady-state simulation, dynamic simulation,optimization, parameter estimation, etc.). Simulators may be configuredto take advantage of the form and structure of the equations for taskssuch as the sensitivity calculations. This configuration allows somecalculations to be performed more robustly and/or efficiently than ispossible when the model is developed as a block of custom computer code.An equation-based process simulation package is software configured tointeract directly with the equations that make up a model. Such asimulator typically parses model equations and builds a representationof the system of equations in memory. The simulator uses thisrepresentation to efficiently perform the calculations requested,whether steady-state simulations, dynamic simulations, optimization,etc. An equation-based process simulation package also allowsincorporation of calculations that are more easily written ascombination of procedures and mathematical equations. Examples mayinclude interpolation within a large data table, calling proprietarycalculation routines distributed as compiled code for which equationsare not available, etc. As newer and better solution algorithms aredeveloped, these algorithms may be incorporated into simulator 300without requiring any changes to the models simulator 300 is configuredto solve.

According to an exemplary embodiment, simulator 300 may be a processsimulator. Process simulators generally include a variety of solutionalgorithms such as reverse mode automatic differentiation, the staggeredcorrector method for variable sensitivities, automatic model indexreduction, robust Newton iteration for solving nonlinear systems frompoor initial values, error-free scaling of variable systems, and theinterval arithmetic method for locating state events. Process simulatorsutilize sparse linear algebra routines for direct solution of linearsystems. The sparse linear algebra routines can efficiently solve verylarge systems (hundreds of thousands of equations) without iteration.Process simulators further provide a particularly strong set ofoptimization capabilities, including non-convex mixed integer non-linearproblems (MINLPs) and global variable optimization. These capabilitiesallow simulator 300 to solve optimization problems using the modeldirectly. In particular, the staggered corrector algorithm is aparticularly efficient method for the sensitivities calculation, whichis often the bottleneck in the overall optimization calculation.

Variable inputs for optimization to be solved by simulator 300 mayinclude both fixed and time-varying parameters. Time varying parametersare typically represented as profiles given by a set of values atparticular times using a specific interpolation method, such aspiecewise constant, piecewise linear, Bezier spline, etc.

Simulator 300 and the associated models may be configured and structuredto facilitate periodic updating. According to an exemplary embodiment,simulator 300 and the associated models may be implemented as a dynamiclink library (DLL). Advantageously, a DLL may be easily exported but notviewed or modified in any structural way.

Requirements engine 310 may be any system or process configured toreceive animal information input and generate animal requirements byapplying one or more requirements models to the set of animalinformation input. A requirements model may be any projection ofpotential outputs based upon any of a variety of set of inputs. Themodel may be as simple as a correlation relating milk production to netenergy in an animal feed or as complex as a variable model computing thenutrient requirement to maximize the productivity of a shrimpaquaculture pond ecosystem. Requirements engine 310 may be configured toselect from a plurality of models based on the animal informationinputs. For example, requirements engine 310 may include models forswine requirements, dairy requirements, companion animal requirements,equine requirements, beef requirements, general requirements, poultryrequirements, aquaculture animal requirements, etc. Further, each modelmay be associated with a plurality of models based on an additionalcategorization, such as developmental stage, stress level, etc.

Animal requirements generated by requirements engine 310 may include alisting of nutrient requirements for a specific animal or group ofanimals. Animal requirements may be a description of the overall diet tobe fed to the animal or group of animals. Animal requirements furthermay be defined in terms of a set of nutritional parameters(“nutrients”). Nutrients and/or nutritional parameters may include thoseterms commonly referred to as nutrients as well as groups ofingredients, microbial measurements, indices of health, relationshipsbetween multiple ingredients, etc. Depending on the degree ofsophistication of system 100, the animal requirements may include arelatively small set of nutrients or a large set of nutrients. Further,the set of animal requirements may include constraints or limits on theamount of any particular nutrient, combination of nutrients, and/orspecific ingredients. Advantageously, constraints or limits are usefulwhere, for example, it has been established at higher levels of certainnutrients or combination of nutrients could pose a risk to the health ofan animal being fed. Further, constraints may be imposed based onadditional criteria such as moisture content, palatability, etc. Theconstraints may be minimums or maximums and may be placed on the animalrequirement as a whole, any single ingredient, or any combinationingredients. Although described in the context of nutrients, animalrequirements may include any requirements associated with an animal,such as space requirements, heating requirements, etc.

Additionally, animal requirements may be generated that define ranges ofacceptable nutrient levels. Advantageously, utilizing nutrient rangesallows greater flexibility during animal feed formulation, as will bedescribed further below with reference to FIG. 3.

Requirements engine 310 may be further configured to account for varyingdigestibility of nutrients. For example, digestibility of some nutrientsdepends on the amount ingested. For example, wherein an animal ingests aquantity of phosphorous in a diet, the percentage that is utilized bythe animal may decrease in relation to the quantity ingested. Ananimal's digestive tract may only be able utilize a certain amount ofphosphorous and the remainder will be passed through the animal.Accordingly, phosphorous utilization may have an inverse relationshipwith the amount of phosphorous in an animal feed after a certain levelis reached. Digestibility may further depend on the presence or absenceof other nutrients, microbes and/or enzymes, processing effects (e.g.gelatinization, coating for delayed absorption, etc.), animal productionor life stage, previous nutrition level, etc. Simulator 300 may beconfigured to account for these effects. For example, simulator 300 maybe configured to adjust a requirement for a particular nutrient based onanother particular nutrient additive.

Requirements engine 310 may also be configured to account for varyingdigestion by an animal. Animal information inputs may includeinformation indicating the health of an animal, stress level of ananimal, reproductive state of an animal, methods of feeding the animal,etc. as it affects ingestion and digestion by an animal. Shifts based onimmune status may cause an increased maintenance cost to engageprotective systems, while reducing voluntary nutrient intake. Forexample, the stress level of an animal may decrease the overall feedintake by the animal, while gut health may increase or decrease a rateof passage. According to another example, changes in a microbial profilefor an animal may indicate a shift in digestion of nutrients fromenzymatic digestion to bacterial fermentation.

Table 2 below includes an exemplary listing of nutrients that may beincluded in the animal requirements. According to an exemplaryembodiment, within the animal requirements, each listed nutrient may beassociated with a value, percentage, range, or other measure of amount.The listing of nutrients may be customized to include more, fewer, ordifferent nutrients based on any of a variety of factors, such as animaltype, animal health, nutrient availability, etc.

TABLE 2 Nutrients Suitable for Generating Animal Requirements ADF AnimalFat Ascorbic Acid Arginine (Total Ash Biotin and/or Digestible) CalciumCalcium/Phos ratio Chloride Choline Chromium Cobalt Copper Cystine(Total Dry Matter and/or Digestible) Fat Fiber Folic Acid HemicelluloseIodine Iron Isoleucine (Total Lactose Lasalocid and/or Digestible)Leucine (Total Lysine (Total Magnesium and/or Digestible) and/orDigestible) Manganese Margin Methionine (Total and/or Digestible)Moisture Monensin NDF NEg (Net Energy NEl (Net Energy NEm (Net Energyfor Gain) Lactation) for Maintenance) NFC (Non-Fiber NiacinPhenylalanine (Total Carbohydrate) and/or Digestible) PhosphorusPhosphate Potassium Protein Pyridoxine Rh Index (Rumen Health Index)Riboflavin Rough NDF Rum Solsug (Rumen Soluble Sugars) Rumres NFC(Ruminant RUP (Rumen Salt Residual Non-Fiber Undegradable Carbohydrate)Protein) Selenium Simple Sugar Sodium Sol RDP (Soluble Rumen Sulfur ME(Metabolizable Degradable Protein) Energy) Thiamine Threonine (TotalTotal RDP and/or Digestible) Tryptophan (Total Valine (Total Vitamin Aand/or Digestible) and/or Digestible) Vitamin B12 Vitamin B6 Vitamin DVitamin E Vitamin K Zinc Gut Health Index Fatty Acids (EPA, CholesterolDHA, Linolenic, etc.) Phospholipids UFC

Requirements engine 310 may be configured to generate the animalrequirements based on one or more requirement criteria. Requirementcriteria can be used to define a goal for which the requirement shouldbe generated. For example, exemplary requirement criteria can includeeconomic constraints, such as maximizing production, slowing growth tohit the market, or producing an animal at the lowest input cost. Theanimal requirements may be used to generate an animal feed formulationfor an animal. Accordingly, the animal requirements may be used asanimal feed formulation inputs.

The requirements engine 310 may further be configured to generate theanimal requirements based on one or more dynamic nutrient utilizationmodels. Dynamic nutrient utilization may include a model of the amountof nutrients ingested by an animal feed that are utilized by an animalbased on information received in the animal information inputs, such asanimal health, feeding method, feed form (mash, pellets, extruded,particle size, etc.), water stability of feed, uneaten food, watertemperature and its impact on enzyme levels, etc. Nutrient utilizationmay further depend on the presence or absence of other nutrientadditives, microbes and/or enzymes, processing effects (e.g.gelatinization, coating for delayed absorption, etc.), animal productionor life stage, previous nutrition level, etc.

Simulator 300 may be configured to account for these effects. Forexample, simulator 300 may be configured to adjust the level of aparticular nutrient, defined in an animal feed formulation input, fromthe level determined based on the animal requirement to a differentlevel based on the presence or absence of another particular nutrient.Using the above example for phosphorous, the amount of phosphorous thatis utilized by an animal may also be affected by other nutrients in theanimal's diet. For example, the presence of a particular microbe in ananimal's digestive track, whether naturally present or added as anutrient, may actually increase the phosphorous utilization beyond thelevels that would normally occur and reduce the amount that enters ananimal's waste stream.

Accordingly, an animal feed formulation input may be modified based onthe nutrient utilization model. However, this change in the animal feedformulation may have an effect on the animal feed formulation, includingthe animal feed formulation that was just modified. Accordingly,compensating for a nutrient utilization model may require an iterativecalculation, constantly updating values, to arrival at a final valuethat is within a predefined tolerance.

Requirements engine 310 may also be configured to account for variationsin digestion and utilization of nutrients by an animal. Animalinformation inputs may include information indicating the health of ananimal, stress level of an animal, reproductive state of an animal,methods of feeding the animal, etc. as it affects ingestion anddigestion by an animal. For example, the stress level of an animal maydecrease the overall feed intake by the animal, while gut health mayincrease or decrease a rate of passage. Alternatively, a stress levelmay alter the actual metabolism for an animal. For example, an animal'smetabolism may be altered by a stress-induced release of cortisone.Other exemplary metabolic modifiers may include immune system cascadesof prostaglandins and other pro-inflammatory cytokines, leukocytes,antibodies, and other immune cells and substances, growth promotingimplants, and adrenergic feed additives. These reactions shift site andextent of digestion, change nutrient intake, and force digestednutrients towards a more catabolic state.

Animal performance simulator 320 may be a process or system including aplurality of models similar to the models described above with referenceto requirements engine 310. The models utilized in animal performancesimulator 320 receive an animal feed formulation from formulator 300through enterprise supervisor 200 and the animal information inputs andapply the models to the feed formulation to produce one or more animalperformance projections. The animal performance projection may be anypredictor of animal productivity that will be produced given the animalfeed formulation input and other input variables.

Environment performance simulator 330 may be a process or systemincluding a plurality of models similar to the models described abovewith reference to requirements engine 310. The models utilized inenvironment performance simulator 330 receive animal feed formulationfrom formulator 300 through enterprise supervisor 200 and apply themodels to the feed formulation and animal information inputs to producea performance projection based on environmental factors. Theenvironmental performance projection may be any prediction ofperformance that will be produced given the animal feed formulationinput, animal information inputs, and environmental factors.

Economic performance simulator 340 may be a process or system includinga plurality of models similar to the models described above withreference to requirements engine 310. The models utilized in economicperformance simulator 340 receive animal feed formulation fromformulator 300 through enterprise supervisor 200 and apply the models tothe feed formulation and animal information inputs to produce aperformance projection based on economic factors. The economicperformance projection may be any prediction of performance that will beproduced given the animal feed formulation input, animal informationinputs, and the economic factors.

The performance projections may include a wide variety of informationrelated to outputs produced based on the provided set inputs. Forexample, performance projections may include information related to theperformance of a specific animal such as the output produced by ananimal. The output may include, for example, the nutrient content ofeggs produced by the animal, qualities associated with meat produced bythe animal, the contents of waste produced by the animal, the effect ofthe animal on an environment, etc.

According to exemplary embodiment, simulators 320, 330, and 340 may berun in parallel or in series to produce multiple performanceprojections. The multiple animal performance projections may remainseparated or be combined into a single comprehensive performanceprojection. Alternatively, performance projections may be generatedbased on a single simulator or a combination of less than all of thesimulators.

Requirements engine 310 may further include additional simulators asneeded to generate performance projections that are customized tosatisfy a specific user criteria. For example, requirements engine 310may include a bulk composition simulator, egg composition simulator,meat fat composition, waste output simulator, maintenance energycalculator, etc.

Referring now to FIG. 4, a general block diagram illustrating aningredients engine 400 and a formulator 500 is shown, according to anexemplary embodiment. Ingredients engine 400 is configured to exchangeinformation with formulator 500. Ingredients engine 400 and formulator500 are generally configured to generate an animal feed formulationbased on available ingredients and received animal requirements.

Ingredients engine 400 includes one or more listings of availableingredients at one or more locations. The listing further includesadditional information associated with the ingredients, such as thelocation of the ingredient, nutrients associated with the ingredient,costs associated with the ingredient, etc.

Ingredients engine 400 may include a first location listing 410, asecond ingredient location listing 420, and a third ingredient locationlisting 430. First ingredient listing 410 may include a listing ofingredients available at a first location, such as ingredients at auser's farm. The second ingredient listing 420 may include a listing ofingredients that are available for purchase from an ingredient producer.Third ingredient listing 430 may include a listing of ingredients thatare found in a target animal's environment such as forage in a pasture,plankton (zooplankton, phytoplankton, etc.), or small fish in anaquaculture pond, etc. The listing of ingredients may further includeenvironmental nutrient inputs. Environmental nutrient inputs may be anynutrient or nutrients that are received and/or utilized by an animalthat is not fed to the animal.

Referring now to third ingredient listing 430, an example of a listingof ingredients that are found in a target animal's environment mayinclude a listing of the mineral content of water. An animal's totalwater consumption can be estimated based on known consumption ratios,such as a ratio of water to dry feed matter consumed. Consumption of aningredient or nutrient may include actual consumption as well as receiptby an animal through absorption, generation through body processes, etc.This ratio may be either assigned an average value or, more preferably,calculated from known feed and animal properties. The mineral content ofthe water provided by producer may be measured on-site. This water, withmeasured mineral content and calculated intake level, may beincorporated in third ingredient listing 430. Although mineral contentis provided as an example, it should be understood that the listing ofingredient may include any nutrient level or characteristic of the watersuch as the water pH level.

Alternatively, third ingredient listing 430 may include an aquaticecosystem total nutrient content. The ecosystem contribution to totalnutrition may be included in several ways. For example, a sample may bedrawn and analyzed for total nutrient content and included as thirdlisting 430. Preferably, the models solved in simulator 300 may beexpanded to include not only that species being produced but otherspecies that live in the ecosystem as well. The model may include one ormore of the following effects: other species competition for feed,produced species consumption of other species in ecosystem, and otherspecies growth over time in response to nutrient or toxin excretion,temperature, sunlight, etc. The models may further account forconsumption/utilization of the environmental nutrient inputs based onthe life stage of the animal, knowledge of growing conditions, analysisof ingredients, etc.

Further, third ingredient listing 430 may be representative of a closednutrient system, wherein outputs generated from an animal feed being fedto an animal are treated as inputs to generate third ingredient listing430. For example, an animal may be initially fed a diet composed ofnutrients from first ingredient listing 410 and/or second ingredientlisting 420. The animal's utilization of the nutrient composition may bedetermined within simulator 300, described in further detail below, andprovided to formulator 500 for optimization versus established animalrequirements. Simulator 300 may further be configured to generate aprojection of the quantity and quality of nutrients that are notutilized by the animal and/or nutrients in the animal's waste that areprovided to the animal's environment.

The output of un-utilized nutrient or waste stream nutrients may then beused for projecting changes to the animal's environment and thecomposition of third ingredient listing 430. For example, where theanimal is an aquatic animal, such as a shellfish, the output from theshellfish may be used in calculating projected changes in the algaestanding stock. This modified algae standing stock is then considered aningredient in third ingredient listing 430 to the extent that theanimals consume the algae standing stock as part of its diet. Theadditional ingredient may reduce or otherwise modify the animal'scalculated requirements. It can be appreciated how the above describedinteraction may be used to create a number of cyclical feedback loops tooptimize the animal production. Further, an optimized animal feed may beoptimized based on the requirements of the entire ecosystem biomass inaddition to the animal.

According to yet another exemplary embodiment, the performanceprojections generated by simulator 300 may be used to estimate thebiomass and nutrient content of a first species, that is a food sourcefor a second species. The first species may be algal, bacterial,invertebrate, or vertebrate. Accordingly, the output of simulator 300may be used to define the ingredients in third ingredient listing 430,including bioavailability and total nutrient provision. For example,wherein the first species is brine shrimp and the second species is anaquarium salt water fish, simulator 300 may be utilized to generate arecommendation for optimizing the growth rate and/or nutrient content ofthe brine shrimp. The brine shrimp population may also be calculated inview of feeding projections for the salt water aquarium fish. Thesebrine shrimp may then be components within third ingredient listing 430and may be used as components in formulating an optimized animal feedfor the salt water aquarium fish. Specifically, the ingredients in thirdingredient listing 430 may be provided to variable nutrient engine 450,discussed below, and formulator 500. Further, the performanceprojections associated with the first animal may be used to projectfuture components within third ingredient listing 430 and theircharacteristics.

As shown in the above example, simulator 300, in combination with thirdingredient listing 430, may be used to model an entire interactionbetween an animal, the organisms in its environment, and the environmentitself. The interaction may be used to satisfy current animalrequirements and to generate projections for the animal, otherorganisms, and the environment.

For example, the environment of third ingredient listing 430 may includeingredients and associated nutrients within a wheat grass pasture. Thepasture may be fertilized with nitrogen, potassium, and phosphorus. Thefertilizer may be naturally occurring, such as from cow manure orpoultry litter, or man-made, such as a chemical fertilizer.

The pasture may be managed by an animal producer such that the wheatgrass does not get more mature than an early boot stage, an optimummaturity for nutrient quality. Upon maturity, the pasture may be grazedby 400 pound stocker calves for about two months. It is recognized thatthe animal, during grazing will generally fertilize the wheat grassnaturally. As the calves graze they will continuously gain weight, whichis made up primarily of minerals, water, and protein. Accordingly, thenitrogen, potassium, and phosphorus that is used to fertilize the wheatgrass become a nutritional component of the calves.

After the cattle are removed from the pasture, the animal producer maychoose to allow the wheat grass to grow to maturity for harvesting. Theharvested wheat grass may be turned directly into another food source,such as flour for bread, or it may be used as bedding in a feedlot.Wheat grass used for bedding may eventually be collected from thefeedlot, along with manure from the cattle in the feedlot and put backin the pasture. The nutrients in the straw and manure may be disked downinto the field and are taken up by the roots of the next crop of wheatgrass.

Accordingly, system 100, using simulator 300, may be configured toiteratively analyze variable inputs that effect not only the animals,but also the environment of the animal, which may in turn affect theanimals. Each projection by simulator 300 may be iteratively performedto determine the effects on related inputs based on the currentprojections.

Third ingredient listing 430 may further include performance projectionsgenerated by simulator 300. For example, the nutrient content of milkmay be modeled for the particular animals for an individual producer.This milk nutrient content model may be used as a third ingredientlisting 430 for consumption by a nursing animal.

Each listing of ingredients may further include additional informationassociated with the ingredients. For example, a listing of ingredientsmay include a listing of costs associated with that ingredient.Alternatively, an ingredient at the first location may include a costsassociated with producing the ingredient, storing the ingredient,dispensing the ingredient, etc., while an ingredient at the secondlocation may include a cost associated with purchasing the ingredient,and an ingredient at the third location may include a cost associatedwith increasing the biomass, changing the nutrient profile, alteringnutrient availability, etc. The additional information may include anytype of information that may be relevant to later processing steps.

Table 3 below includes an exemplary list of ingredients which may beused in generating the animal feed formulation. The listing ofingredients may include more, fewer, or different ingredients dependingon a variety of factors, such as ingredient availability, entry price,animal type, etc.

TABLE 3 Exemplary Ingredients Suitable for Use in Formulating CustomFeed Mixes Acidulated Soap Active Dry Yeast Alfalfa Meal Stocks Alfalfa-Alimet Alka Culture Dehydrated Alkaten Almond Hulls Ammonium ChlorideAmmonium Lignin Ammonium Ammonium Polyphosphate Sulfate Amprol AmprolEthopaba Anhydrous Ammonia Appetein Apramycin Arsanilic Acid AscorbicAcid Aspen Bedding Avizyme Bacitracin Zinc Bakery Product BarleyBarley-Crimped Barley-Ground Barley-Hulless Barley-Hulls Barley-MiddsBarley-Needles Barley-Rolled Barley-Whole Barley-With Baymag Enzyme BeetBeet Pulp Biotin Biscuit By Product Black Beans Blood-Flash Dry BoneMeal Brewers Rice Brix Cane Buckwheat Cage Calcium Calcium Cake CalciumChloride Calcium Formate Calcium Iodate Calcium Sulfate Calcium PropCanadian Peas Cane-Whey Canola Cake Canola Fines Canola Meal Canola OilCanola Oil Canola Oil Mix Blender Canola Screenings Canola-WholeCarbadox Carob Germ Carob Meal Cashew Nut Byproduct Catfish Offal MealCholine Chloride Chromium Tripicolinate Citrus Pulp Clopidol CobaltCobalt Carbonate Cobalt Sulfate Cocoa Cake Cocoa Hulls Copper OxideCopper Sulfate Corn Chips Corn Chops Corn Coarse Cracked Corn- CoarseGround Corn Cob-Ground Corn Distillers Corn Flint Corn Flour Corn GermBran Corn Germ Meal Corn Gluten Corn- High Oil Corn Kiblets Corn MealDehulled Corn Oil Corn Residue Corn Starch Corn/Sugar Blend Corn-CrackedCorn-Crimped Corn-Ground Fine Corn-Ground Roasted Corn-Steam FlakedCorn-Steamed Corn-Whole Cottonseed Culled Cottonseed Hull CottonseedMeal Cottonseed Oil Cottonseed Whole Coumaphos Culled Beans DanishFishmeal Decoquinate Dextrose Diamond V Yeast Disodium PhosphateDistillers Grains Dried Apple Pomace Dried Brewers Yeast DriedDistillers Dried Porcine Milo Dried Whole Milk Duralass Enzyme BoosterPowder Epsom Salts Extruded Grain Extruded Soy Flour Fat Feather MealFeeding Oatmeal Fenbendazole Fermacto Ferric Chloride Ferrous CarbonateFerrous Carbonate Ferrous Sulfate Fine Job's Tear Fish Meal Bran FishFlavoring Folic Acid Fresh Arome Fried Wheat Noodles Gold Dye GoldFlavor Grain Dust Grain Screening Granite Grit Grape Pomace Green DyeGreen Flavor Guar Gum Hard Shell Hemicellulose Extract Herring MealHominy Hygromycin Indian Soybean Meal Iron Oxide-Red Iron-Oxide YellowJob's Tear Broken Kelp Meal Seeds Kem Wet Lactose Larvadex LasalocidLevams Hcl Limestone Linco Lincomix Lincomycin Linseed Meal Liquid FishLupins Solubles Lysine Magnesium Magnesium Sulfate Malt Plant By-Manganous Ox Maple Flavor Products Masonex Meat And Bone Meat Meal MealMepron Methionine Millet Screenings Millet White Millet-Ground MiloBinder Milo-Coarse Ground Milo-Cracked Milo-Whole Mineral Flavor MineralOil Mixed Blood Meal Molasses Molasses Blend Molasses Dried MolassesStandard Molasses Standard Molasses-Pellet Beet Cane Mold MonensinMonoamonum Phos Monosodium Monosodium Mung Bean Hulls GlutamatePhosphate Mustard Meal High Mustard Oil Mustard Shorts Fat NarasinNatuphos Niacin Nicarbazin Nitarsone Oat Cullets Oat Flour Oat GroatsOat Hulls Oat Mill Byproducts Oat Screenings Oat Whole Cereal OatmillFeed Oats Flaked Oats-Ground Oats-Hulless Oats-Premium Oats-RolledOats-Whole Oyster Shell Paddy Rice Palm Kernel Papain Papain EnzymePaprika Spent Meal Parboiled Broken Pea By-Product Rice Pea Flour PeanutMeal Peanut Skins Pelcote Dusting Phosphate Phosphoric Acid PhosphorusPhosphorus Pig Nectar Defluorinated Poloxalene Popcorn Popcorn PorcinePlasma; Pork Bloodmeal Screenings Dried Porzyme Posistac PotassiumBicarbonate Potassium Potassium Potassium Carbonate Magnesium SulfateSulfate Potato Chips Poultry Blood/ Poultry Blood Feather Meal MealPoultry Byproduct Predispersed Clay Probios Procain Penicillen PropionicAcid Propylene Glycol Pyran Tart Pyridoxine Quest Anise Rabon RapeseedMeal Red Flavor Red Millet Riboflavin Rice Bran Rice By-Products RiceDust Rice Ground Fractions Rice Hulls Rice Mill By- Rice Rejects ProductGround Roxarsone Rumen Paunch Rumensin Rye Rye Distillers Rye WithEnzymes Safflower Meal Safflower Oil Safflower Seed Sago MealSalinomycin Salt Scallop Meal Seaweed Meal Selenium Shell Aid ShrimpByproduct Silkworms Sipernate Sodium Acetate Sodium Benzoate SodiumSodium Molybdate Sodium Bicarbonate Sesquicarbonate Sodium SulfateSolulac Soy Flour Soy Pass Soy Protein Concentrate Soybean Cake SoybeanCurd By- Soybean Dehulled Product Milk By-Product Soybean Hulls SoybeanMill Run Soybean Oil Soybean Residue Soybeans Extruded Soybeans-RoastedSoycorn Extruded Spray Dried Egg Standard Micro Premix Starch MolassesSteam Flaked Corn Steam Flaked Wheat Sugar (Cane) Sulfamex-Ormeto SulfurSunflower Meal Sunflower Seed Tallow Fancy Tallow-Die Tallow-MixerTapioca Meal Tapioca Promeance Taurine Terramycin Thiabenzol ThiamineMono Threonine Tiamulin Tilmicosin Tomato Pomace Trace Min TricalciumTriticale Phosphate Tryptophan Tryptosine Tuna Offal Meal Tylan TylosinUrea Vegetable Oil Blend Virginiamycin Vitamin A Vitamin B ComplexVitamin B12 Vitamin D3 Vitamin E Walnut Meal Wheat Bran Wheat CoarseGround Wheat Germ Meal Wheat Gluten Wheat Meal Shredded Wheat MillrunWheat Mix Wheat Noodles Low Wheat Red Dog Wheat Starch Fat Wheat StrawWheat With Wheat-Ground Enzyme Wheat-Rolled Wheat-Whole Whey Dried WheyPermeate Whey Protein Whey-Product Concentrate Dried Yeast Brewer DriedYeast Sugar Cane Zinc Zinc Oxide Zoalene

Ingredient engine 400 may further include an ingredient informationdatabase 440. Ingredient information database 440 may include any kindof information related to ingredients to be used in generating the feedformulation, such as nutrient information, cost information, userinformation, etc. The information stored in database 440 may include anyof a variety of types of information such as generic information,information specifically related to the user, real-time information,historic information, geographically based information, etc. Ingredientinformation database 440 may be utilized by ingredient engine 400 tosupply information necessary for generating an optimized feedformulation in conjunction with information supplied by the user.

Ingredient information database 440 may further be configured to accessexternal databases to acquire additional relevant information, such asfeed market information. Feed market information may similarly includecurrent prices for ingredient, historical prices for output, ingredientproducer information, nutrient content of ingredient information, markettiming information, geographic market information, delivery costinformation, etc. Ingredient information database 440 may further beassociated with a Monte Carlo type simulator configured to providehistorical distributions of ingredient pricing and other informationthat can be used as inputs to other components of system 100.

Ingredient engine 400 may further include a variable nutrient engine 450configured to provide tracking and projection functions for factors thatmay affect the nutrient content of an ingredient. For example, variablenutrient engine 450 may be configured to project the nutrient contentfor ingredients over time. The nutrient content for some ingredients maychange over time based on method of storage, method of transportation,natural leaching, processing methods, etc. Further, variable nutrientengine 450 may be configured to track variability in nutrient contentfor the ingredients received from specific ingredient producers toproject a probable nutrient content for the ingredients received fromthose specific ingredient producers.

Variable nutrient engine 450 may be further configured to account forvariability in nutrient content of ingredients. The estimation ofvariability of an ingredient may be calculated based on informationrelated to the particular ingredient, the supplier of the ingredient,testing of samples of ingredient, etc. According to exemplaryembodiment, recorded and/or estimated variability and covariance may beused to create distributions that are sampled in a Monte Carlo approach.In this approach, the actual nutrient content of ingredients in anoptimized feed formulation are sampled repeatedly from thesedistributions, producing a distribution of nutrient contents. Nutrientrequirements may then be revised for any nutrients for which thenutrient content is not sufficient. The process may be repeated untilthe desired confidence is achieved for all nutrients. The actualnutrient content for the ingredients may be used to generate an animalfeed formulation for an animal. Accordingly, the nutrient content forthe ingredients may also be used as animal feed formulation inputs.

Referring now to formulator 500, formulator 500 is configured to receiveanimal requirements from simulator 300 through enterprise supervisor 200and nutrient information from ingredients engine 400 based on availableingredients and generate an animal feed formulation. Formulator 500calculates a least-cost feed formulation that meets the set of nutrientlevels defined in the animal requirements.

The least-cost animal feed formulation may be generated using linearprogramming optimization, as is well-known in the industry. Theleast-cost formulation is generally configured to utilize a usersavailable ingredients in combination with purchased ingredients tocreate an optimized feed formulation. More specifically, the linearprogramming will incorporate nutrient sources provided by a user such asgrains, forages, silages, fats, oils, micronutrients, or proteinsupplements, as ingredients with a fixed contribution to the total feedformulation. These contributions are then subtracted from the optimalformulation; the difference between the overall recipe and theseuser-supplied ingredients constitute the ingredient combinations thatwould be produced and sold to the customer.

Alternatively, the formulation process may be performed as a Monte Carlosimulation with variability in ingredient pricing included as eitherhistorical or projected ranges to created distribution which aresubsequently optimized as described above.

Referring now to FIG. 5, a flowchart illustrating a method 600 foranimal production optimization is shown, according to an exemplaryembodiment. Method 600 generally includes identifying optimized valuesfor one or more animal information inputs according to at least oneoptimization criteria. Although the description of method 600 includesspecific steps and a specific ordering of steps, it is important to notethat more, fewer, and/or different orderings of the steps may beperformed to implement the functions described herein. Further,implementation of a step may require reimplementation of an earlierstep. Accordingly, although the steps are shown in a linear fashion forclarity, several loop back conditions may exist.

In a step 605, enterprise supervisor 200 is configured to receive theanimal information inputs. The animal information inputs can be receivedfrom a user through user interface 210, populated automatically based onrelated data, populated based on stored data related to the user, orreceived in a batch upload from the user. The received animalinformation inputs include a designation of one or more of the animalinformation inputs as a variable input. The designation as a variableinput may be received for single, multiple, or all of the animalinformation inputs.

In a step 610, enterprise supervisor 200 is configured to receive anoptimization criteria through user interface 210 or, alternatively,receive a preprogrammed optimization criteria. The optimization criteriamay include maximizing productivity, reducing expenses, maximizingquality of output, achieving productivity targets, etc. In an exemplaryembodiment, the optimization criteria may be an objective functionrequiring minimization or maximization. The objective function may haveconstraints incorporated therein or may be subject to independentconstraints. The objective function may be a function of any combinationof variables of the animal production system.

In a step 615, enterprise supervisor 200 is configured to communicatethe animal information inputs and optimization criteria to simulator300. Upon receiving the animal information inputs and optimizationcriteria, simulator 300 is configured to generate a set of animalrequirements in a step 620.

In a step 625, the set of animal requirements are communicated fromsimulator 300 through enterprise supervisor 200 to formulator 500.Formulator 500 is configured to generate a least cost animal feedformulation based upon the animal requirements and nutrient informationreceived from nutrient engine 450 in a step 630. The least cost animalfeed formulation may be determined based at least in part on thecomponents within the animals environment, represented by thirdingredient listing 430.

In a step 635, enterprise supervisor 200 is configured to generateoptimized values for the one or more variable inputs received in step605, as discussed in detail above with reference to FIG. 2.

Although specific functions are described herein as being associatedwith specific components of system 100, functions may alternatively beassociated with any other component of system 100. For example, userinterface 210 may alternatively be associated with simulator 300according to an alternative embodiment.

Many other changes and modifications may be made to the presentinvention without departing from the spirit thereof. The scope of theseand other changes will become apparent from the appended claims.

1. A system for generating optimized values for variable inputs to ananimal production system, comprising: a simulator engine configured toreceive a plurality of animal information inputs and generate aperformance projection, wherein at least one of the animal informationinputs is designated as a variable input; and an enterprise supervisorengine configured to generate an optimized value for the at least onevariable input based on an optimization criteria for at least one targetoutput characteristic.